Method and apparatus for diagnosing joints

ABSTRACT

A method and apparatus for diagnosing joints based on sensed joint vibrations. Accelerometers disposed on the skin adjacent to the joint detect vibrational patterns during movement of the joint. These patterns are then processed by one processor to generate a predetermined set of data parameters descriptive of the vibration pattern. Also, the position and velocity of the joint during the vibration is recorded. This information from numerous patients with known joint conditions is used to train a adaptive interpreter, such as a neural network, to produce an output in response to these inputs which is indicative of the known joint condition. Once trained, the adaptive interpreter can then interpret this set of parameters for an unknown joint to generate a fast and reliable diagnosis. The result is a non-subjective joint disorder classification system that can be utilized by persons without particular expertise in analyzing joint vibrational patterns.

BACKGROUND OF THE INVENTION

1. Technical Field

This invention relates to a system and method for diagnosing jointconditions, and more particularly to a system and method forautomatically analyzing vibrations from moving joints to classify jointconditions.

2. Discussion

The effective treatment of joint disease depends on an accuratediagnosis. Usually the most accurate diagnostic tool is direct viewingof the joint using invasive surgical techniques. Unfortunately, therisks and costs associated with surgical diagnostic techniques areprohibitive for all but the most serious categories of conditions. Forexample, arthroscopy of the knee typically costs between $5,000.00 and$7,000.00.

A second choice in joint diagnosis is the use of radiological imagingtechniques. These include x-rays and Computed Tonography (CT) scans,magnetic resonance imaging (MRI) and ultrasound. These techniques areused with varying degrees of success. CT scans and MRI are relativelyexpensive (about $500-$1,500.00) and sometimes do not reveal adequateinformation about the condition of the joint to permit an accuratediagnosis. In brief, neither surgery nor imaging techniques offer anideal joint diagnostic tool for many joint disorders.

A third technique for diagnosing joints relies on the interpretation ofvibrations emitted by joints. In general, the term "auscultation" isused to describe any method of examination of the functions andconditions of the human body by the sounds or vibrations they produce.Physicians have listened to sounds and felt vibrations from human jointsin diagnosing joint pathology for centuries. Unfortunately, thisapproach has often proved to be frequently inaccurate. This is primarilydue to the subjective nature of the use of hands and ears as vibrationsensors. Another difficulty has been the limitations of language incommunicating the types of sounds generated from joints associated withparticular joint diseases from one practitioner to another. Also,auscultation depends upon the widely varying expertise of the examiner.Thus, while the characterization of joint conditions by analyzing thesounds produced by the joint shows promise as a diagnostic tool, a moreobjective approach than simply listening to the sounds is required toachieve the desired levels of reliability.

To overcome the shortcomings of auscultation, techniques forelectronically recording joint vibrations or sounds have been developed.Once recorded, a visual display of the sounds can be generated toprovide a more objective means for comparing the sounds from a patient'sjoint with those of joints having known pathologies. The first attemptsto record joint sounds utilized microphones attached to the skinadjacent the joint. One problem with the use of microphones has been thedifficulty in distinguishing articular sounds from extrinsic sounds,such as snapping tendons, noise due to hand tremors, skin friction andcommon background noise. This is because microphones integrate soundarising from a region of space, lacking a focus point, and precisevibration measurement at a point. Also some low frequency jointvibrations are below the dynamic range of microphones and could nottherefore be detected.

For these reasons, accelerometers (or velocity transducers) havereplaced microphones as the preferred sensors for recording jointsounds. This is because accelerometers have the mechanical advantage ofbeing able to detect the direct transmission of vibrations. Anaccelerometer consists of a case within which is a piezoelectric crystalthat has a mass resting on it. This crystal reacts to acceleration byproducing a minute electric charge between its top and bottom surfaces,due to the compression produced by the mass, which is directlyproportional to the acceleration. As a result, accelerometers detectonly localized vibration and are sensitive to activity of very smallamplitude.

The accelerometer is the basis of the new technique for joint diagnosiscalled vibration arthrometry. With vibration arthrometry reliablerecordings of joint sounds and vibrations can be recorded and displayed.Accurate diagnosis can often be accomplished by comparing the vibrationsfrom a patient's joint with those previously recorded from joints havingparticular known conditions. Nevertheless, subjective visual evaluationof the vibration waveform is still required to classify the vibrationpatterns. Also, the visual recognition of patterns is sometimesanecdotal; a perceived waveform may be only coincidentally related to aspecific condition.

To assist in visual analysis, various statistical techniques have beenemployed. These include multiregression analysis, autocorrelation, andfast fourier transform analysis. These techniques are used to findparameters (for example, related to frequency and amplitude) that assistin the classification of joint conditions by their vibration patterns.However, even these statistical techniques ultimately require humaninterpretation to arrive at an accurate classification and diagnosis ofjoint condition. Moreover, a relatively high level of expertise isusually required to accurately interpret the results, limiting theusefulness of these techniques for most clinicians.

Thus, it would be desirable to provide a diagnostic tool for classifyingjoint conditions which is non-invasive, inexpensive and easy to use.Further, it would be desirable to provide a joint diagnostic tool havingthese characteristics which utilizes joint vibrations to arrive at anon-subjective joint disorder classification. Also, it would bedesirable to provide technique for classifying joint conditions by thevibration patterns that can be utilized by persons without particularexpertise in analyzing the joint vibrational patterns where the resultsdo not depend upon the skill of the person conducting the test.

SUMMARY OF THE INVENTION

Pursuant to the present invention a method and apparatus for diagnosingjoints is provided. In accordance with a first aspect of the presentinvention, a system for classifying degenerative joint diseaseconditions includes one or more sensors for detecting a vibrationpattern from the joint. A preprocessor is provided which generates apredetermined set of data parameters descriptive of the vibrationpattern. An adaptive interpreter receives these data parameters as inputand produces an output which indicates at least one classification ofthe degenerative Joint disease condition.

In accordance with another aspect of the present invention a system forclassifying trauma induced joint conditions is provided. This systemincludes a sensor means for detecting a vibration pattern from thejoint. Also, a preprocessor is provided for determining a set of dataparameters which are descriptive of the vibration pattern. An adaptiveinterpreter receives the data parameters as input and produces an outputwhich indicates at least one classification of the trauma inducedcondition of the joint.

In accordance with a third aspect of the present invention a system fordiagnosing conditions of an implantable orthopedic device is providewhich includes a sensor for detecting a vibration pattern from theimplantable device. A preprocessor is provided for producing apredetermined set of data parameters descriptive of the vibrationpattern. An interpreter receives the data parameters as input andprovides an output which indicates at least one implantable orthopedicdevice condition.

In accordance with a fourth aspect of the present invention a system forclassifying joint conditions in a load bearing joint is provided. Thesystem includes sensor means for detecting a vibration pattern from thejoint. Also, a pre-processor is provided for determining a set of dataparameters which are descriptive of the vibration pattern. An adaptiveinterpreter receives the data parameters as input and produces an outputwhich indicates at least one classification of the condition of the loadbearing joint.

In the preferred embodiment, the adaptive interpreter is a neuralnetwork which has been pretrained using vibrational patterns from jointshaving known conditions. Once trained the neural network recognizes andclassifies unknown joint conditions. The result is a non-invasiveobjective and reliable technique for joint diagnosis. The presentinvention can be utilized to quickly and inexpensively provide accuratejoint condition diagnosis. Furthermore, the present invention does notrequire a high level of skill by the practitioner utilizing it. Inparticular, it does not require the subjective interpretation ofvibrational patterns by an expert.

BRIEF DESCRIPTION OF THE DRAWINGS

The various advantages of the present invention will become apparent toone skilled in the art by reading the following specification and byreference to the following drawings in which:

FIG. 1 is a diagram of the various components of a temporomandibularjoint.

FIG. 2 is a diagram of a-technique for measuring and displayingvibrations from the temporomandibular joint in accordance with the priorart.

FIG. 3 is a diagram of a portion of a temporomandibular joint indicatingthe direction of sensed vibrations.

FIGS. 4A-B are diagrams of an apparatus employed to track jaw motionduring measurements of temporomandibular joint (TMJ) vibrations.

FIG. 5 is a system diagram of the main components of a preferredembodiment of the present invention employed to classify vibrationalpatterns from temporomandibular joints.

FIGS. 6A-B are diagrams of two types of TM joint vibrations.

FIG. 7 depicts waveforms comparing left and right TM joint vibrations.

FIGS. 8A-D illustrate various types of vibration outputs produced by thejoint diagnostic system of the present invention shown in FIG. 5.

FIG. 9 is a diagram of the velocity and position of the jaw duringmeasurement as recorded using the jaw tracker apparatus shown in FIG. 4.

FIGS. 10A-C illustrate the display of vibrational patterns gatheredduring repeated opening and closings of a TM joint and alsocorresponding left and right frequency spectrums of the vibrations.

FIG. 11 is a display of the data parameters produced by the preprocessorin FIG. 5.

FIG. 12 is a diagram of one embodiment of the present inventionemploying a multi-layer perceptron adaptive interpreter used with thejoint diagnostic system of the present invention shown in FIG. 5.

FIG. 13 is a schematic diagram of a neural network including inputs andoutputs in accordance with a preferred embodiment of the presentinvention shown in FIG. 5.

FIG. 14 is a diagram of the output classification produced by the jointdiagnostic system shown in FIG. 5.

FIG. 15 is a knee joint diagnostic system in accordance with anotherembodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is a method and apparatus for diagnosing jointsbased on sensed vibrational patterns occurring during movements. Thisinvention is particularly adapted to classifying conditions resultingfrom degenerative joint disease, as well as from traumatic injury. Also,the present invention can be used to detect and classify conditionsexisting in load bearing joints and in implantable orthopedic devices.However, as will be evident from the discussion below, the presentinvention may be easily adapted to the classification of other jointconditions.

In a preferred embodiment of the present invention the diagnostic systemis adapted to classify degenerate joint disease and other conditionsinvolving the temporomandibular (TM) joint. To better understand thevarious conditions and disorders of the TM joint there is shown in FIG.1 a sketch of the main components of the TM joint. The TM joint 10comprises a condyle 12, a disc 14 and an eminence 16. The condyle ispart of the mandible (jaw) and the eminence is part of the cranium. Thedisc has a posterior region 18 and an interior region 20. The disc isattached posteriorly to the cranium by a posterior attachment 22. Also,there are medial and lateral ligaments attaching the disc to the condyle12 which are not shown in FIG. 1. Additional ligaments which are notshown are attached to the eminence 16 to limit the range of motion ofthe condyle.

Referring now to FIG. 2 there is shown a larger view of the cranium 24,and mandible 26, part of which includes the left and right condyle 12.The disc 14 is shown in the space between the eminence 16 and condyle12.

During movement of the TM joint a wide variety of vibrations may beproduced, particularly where there is a TM joint disorder present. Asshown in FIG. 2 in accordance with the prior art these vibrations can bedetected by placing left and right sensors 28 and 30 in contact withskin 32 adjacent to the TM joint 10. Sensors 28 and 30 may comprise, forexample, microphones, or accelerometers, which are preferred overmicrophones for the reasons discussed above. The signals received by thesensors 28 and 30 are converted into electrical signals which may bedisplayed in a variety of display modes as illustrated in the display 34which shows the vibration pattern from the left 36 and right 38 TMjoints respectively. Also, FIG. 2 illustrates the direction of vibrationfor each TM joint. Positive vibration signals are defined as vibrationsin the direction toward the opposite TM joint.

Referring now to FIG. 3 the mandible 26 and condyle 12 are shown in moredetail, along with the left and right sensors 28 and 30. An importantconsideration in analyzing TMJ vibrations is the vibration direction. Asshown in FIG. 3, when lateral vibrations of one condyle occur, theopposite side condyle also vibrates, but 180° out of phase. When onecondyle vibrates in a vertical (up-down) or antero-posterior(forward-back) direction, little or no vibration occurs on the oppositeside. Identifying the existence of a lateral vibration is part of thepre-processing of the system, as described in more detail below. Thisavoids the common clinical problem of diagnosing/attributing a"condition" to the wrong joint.

Another consideration that will be important to the analysis of TM jointvibrations will be the recording of the motion and position of the jawduring the (occurrence) of the vibration. Referring now to FIGS. 4A-Bthere is shown a jaw tracker 40 which the subject wears during vibrationmeasurements in accordance with the present invention. FIG. 4B shows thedirections of motion which the jaw tracker 40 measures. That is, the jawtracker measures translation in the x, y and z directions as well as tworotations; about the x axis and about the z axis. In more detail, thejaw tracker 40 includes a small magnet (not shown) attached to thelabial vestibule which is in front of the lower anterior teeth. Thismagnet is tracked by the tracker 40. The measurements represent theamount of movement of the lower anterior teeth. Thus, if the mandible 26goes to the left or to the right from a starting point this distance ismeasured by the jaw tracker 40. In general the jaw tracker 40 measuresthe movement of the magnet by detecting the changes in the magnetic fluxlevels at 8 sensors 41 and converting them into translations/rotationsof the magnet. The signals from the jaw tracker are processed in acircuit 42 referred to as a bioelectrognathograph (EGN).

Referring now to FIG. 5 an overall schematic block diagram of the jointdiagnostic system 44 in accordance with the present invention is shown.The system includes left and right accelerometers 46 and 48, eachconnected to an amplifier 50 and 52 which amplify the accelerometer'ssignals. These amplified signals are then transmitted to a multiplexerunit 54. Also, the jaw tracker 40 which includes the electrognathographcircuit 42 transmits a pair of signals to the multiplexer 54. The firstof these signals 56 indicates the amount of opening of the jaw; and thesecond signal 58 indicates the left/right position in relation to theteeth together position (occlusion).

It will be appreciated that the multiplexer will transmit signals fromits four inputs in sequence to its output 55. The output 55 of themultiplexer 54 is coupled to an analog to digital converter 60, whereupon it is transmitted to a host computer 62. The host computer maycomprise, for example, a conventional personal computer with a keyboard(not shown). The host computer 62 includes a CPU 63 and a preprocessor65. The host computer 62 is also coupled to an adaptive interpreter 64,which in the preferred embodiment comprises a neural network. Both aprinter 66 and a CRT display 68 are coupled to the host computer 62 fordisplaying the waveforms and diagnostic results as described in moredetail below. It is preferred that all three components in the system 44be optically coupled.

Referring now to FIGS. 6A and B there is shown comparison of two kindsof TM joint vibrations. The raw signal of a tissue displacement TM jointvibration 70 is shown consisting of relatively large amplitude, lowfrequency, vibrations. This kind of vibration is sometimes referred toas a "click". In contrast, a vibration caused by rough surfaces 72 ischaracterized by higher frequency and lower amplitude vibrations. Thistype of vibration is commonly referred to as "crepitus". In FIG. 6Bthese two kinds of TM joint vibrations are shown in the frequencydomain. As expected, the tissue displacement vibration is comprises ofprimarily lower frequencies, while the rough surface vibration containsboth lower and higher frequencies. This illustrates the importance offrequency analysis in characterizing TM joint vibrations.

Referring now to FIG. 7, there is shown a display of the raw signaloutputs from the left sensor 46 in curve 74 and of the right sensor 48in curve 76. Also, the horizontal line 80 indicates the amount ofopening of the jaw as measured by the jaw tracker 40 and the lowerhorizontal line 78 displays the left/right position of the anteriorpoint of the jaw (relative to occlusion) as measured by the jaw tracker40. It should be noted that when comparing the left and right vibrationcurves 74 and 76; the waveforms are very similar except that one is themirror image of the other. Also, the left curve 74 begins slightly aheadof the right curve 76, but the left curve 74 moves up and the rightcurve 76 moves down.

These two curves reveal a phenomenon that occurs when there areleft/right vibrations. Because the sensors are facing each other theyare 180° out of phase, and, as the mandible vibrates left and right thesame waveform is picked up on both sides except for the reversal in thephase. Also, the fact that one curve begins the vibration first andtypically has a larger amplitude reveals that to be the side where thevibration is originating. The side opposite the originating side willhave a signal that is a bit smaller and later in starting. This permitsthe differentiation of which side is originating the vibration. This isa very important consideration, particularly where treatment is calledfor. It is not unheard of for confusion to exist as to which side of thejaw is causing a particular vibration. This is a serious problem whencorrective surgery is commenced on the wrong side. Thus, the vibrationwaveforms 74 and 76 contain information which allows the determinationof which side is producing the vibration. This can be determinedvisually by the operator and signaled to the neural network by a singlekey press. Alternatively, it can be detected either in pre-processing orin the network.

Referring now to FIG. 8A-D a variety of waveforms are shown. In FIG. 8Athere is a waveform similar to the waveform shown in FIG. 7 where thelateral vibration in the opposing side is almost as large as thatmeasured in the originating side. In FIG. 8B the amplitude in theopposing side is only about 50% of the amplitude in the originatingside. In FIG. 8C the vibration in the opposing side is only a smallpercentage of the vibration being transmitted in the originating side.In FIG. 8D the vibration in the opposing side is virtually not existent.These figures illustrate a technique for differentiating the directionof the axis of the vibration within a joint. That is, where the axis ofthe vibration is orthogonal to the opposite TM joint there will bealmost no vibration in the opposite side, as illustrated in FIG. 8D.Similarly, where the axis of vibration is in the direction of theopposite TM joint, there will be considerable conduction of vibration tothe opposite joint as shown in FIG. 8A. This is another illustration ofthe wealth of information about the condition of the joint that isavailable from these vibration curves. This information can be visuallydetected by the operator and entered via the keyboard or detected in thepre-processing stage. A separate neural network could also be used todetect this.

Referring now to FIG. 9, there is shown an x, y positional display ofinformation gathered from the jaw tracker 40. To the right of FIG. 9 isshown a display that is schematically representing viewing the patientfrom the front. Where the patient opens his jaw and deviates to the left(the patient's right). This indicates that the patient has a problem oflack of translation in the right joint, or restriction in translation inthe right joint which causes the patient to deflect to the right onopening. The curve then proceeds in a clockwise direction back to theorigin at the top right upon closing.

The curve in the left of FIG. 9 is a velocity profile. Again, thetracing is drawn on a clockwise manner with the opening velocitydisplayed on the horizontal axis and the vertical position displayed onthe vertical axis. This curve reveals that when the patient opens hismouth wide, initially the mandible accelerates to some peak value atabout 35% opening and then decelerates to 0 at maximum opening. Then,the mandible accelerates during closing up to about mid closure and thendecelerates up to the point of tooth contact at the top of the curve.The small circles are displaying areas where a vibration is occurring.While the velocity curve may be used as a neural network input, in oneembodiment the velocity and jaw position are estimated. This isaccomplished by having the subject open and close his jaw insynchronization with a metronome (or a simulated metronome on the hostcomputer screen). In this way the speed of opening and closing isstandardized. The jaw tracker can be eliminated by using an estimatedposition based on the metronome cycle and range of motion (ROM) value.

Referring now to FIG. 10A there is shown a raw display which includesthe right TM joint vibration curve 82, the opening curve 84, theleft/right position of the jaw 88 and the left joint vibration 90. Inthis display, the patient begins with their teeth together, opens wideand then closes and repeats this for four complete cycles of movement.In the process of analyzing the data, a window 92 is placed over avibration. The window consists of the area between two dotted lines witha third dotted line defining the mid-point. A similar window is placedover each of the four vibration points in each of the four cycles. Thewindows 92 are centered on the onset of the vibration of interest. Inthis way, four samples of the same vibration are obtained. FIGS. 10B andC indicate the left and right frequency spectrums of these fourvibrations respectively. By displaying both the average data and therelative original data, it can be seen how much the vibration is varyingfrom cycle to cycle. In the preferred embodiment, there are 256 datapoints within each window.

Referring now to FIG. 11, there is shown a table of data resulting fromanalysis for the four vibrations in the four windows 92 shown in FIG.10A. Because of variation between the data the four sets of data areaveraged. In more detail, the data displayed in FIGS. 10B and C aredetermined by the preprocessor 63, using calculations including FastFourier Transforms. From the Fast Fourier Transforms various numericvalues are calculated as displayed in FIG. 11. It will be appreciatedthat a Fast Fourier Transform is a well known mathematical method thatbreaks the vibration into its component frequencies. It is based on theprinciple that any signal can be described as the sum of the sine andcosine waves. The preprocessor 63 may comprise one of a number ofconventional statistical analysis software products available such asMATLAB, available from The Math Works, Inc. of Natick, MA. Thepre-processor 63 generates all of the desired statistical parameters.The peak frequency is the frequency that contributes the most power tothe signal. The peak amplitude is the amplitude of the peak frequency.The median frequency is defined as the frequency at 1/2 of total power.The "Distance from C.O." is the distance in millimeters from centricocclusion to the onset of the vibration. "Slant Vert" indicates thedistance from C.O. and "Lat" indicates lateral distance from C.O. Alsoshown in FIG. 11 is the integral parameter which is defined as the areaunder the original data curve of the FFT. Also, a reference frequency of300 hertz is selected and the "integral" below 300 and above 300 hertzis defined, as well as the ratio of these two values.

It will be appreciated that all of the parameters in FIG. 11 aredifferent ways of arriving at numerical indicators of how the energy isdistributed in the vibration patterns. It is useful to arrive at suchcompressed descriptors of the waveform to use the data in accordancewith the present invention. That is, it is desirable to limit the numberof inputs to the neural network 64 used to analyze the patterns.Alternatively, the analysis of the raw signal would require 256 valuesfor every window being analyzed.

In accordance with the present invention the inventors have found thatcertain parameters such as the median frequency, the integral, integralgreater than and less than 300 hertz, and the greater than and less than300 hertz ratio to be particularly useful in characterizing these jointvibration patterns. It is important to find which parameters are relatedto the various classifications of joint conditions and which are not. Inthis regard, it is one of the particular advantages of using a neuralnetwork in the system of the present invention that the neural networkcan inform the researcher of which parameters are useful and which arenot. For example, peak frequency is a commonly fused parameter inprevious attempts to analyze vibrational patterns from joints. However,once the neural network 64 had been trained in accordance with thetechniques of the present invention, as described in more detail below,it was discovered that the parameter of peak frequency was not importantto the classification. That is, the internal weights of the trainedneural network associated with peak frequency were very low. Thus, whilepeak frequency is illustrated in the table in FIG. 11, as a parameter itis of limited usefulness, and satisfactory results may be obtainedwithout using the peak frequency as input to the neural network. Thusthe neural network used with the present invention becomes a researchtool to assist in discovering the most important parameters forvibration characterization.

Referring now to FIG. 12 a schematic diagram of a conventionalmultilayered perceptron neural network 94 in accordance with the presentinvention is shown. It should be noted that the neural network 94 shownin FIG. 12 is illustrated schematically by function but may in practicecomprise a neural network implemented in either hardware or software.For example, in the preferred embodiment, the neural network 64 utilizedwith the preferred embodiment of the present invention comprises astandard neural network software program known as Neuroshell sold byWard Systems Group, Inc. of Frederick, Md.

In one embodiment of the present invention, the neural network 94comprises a multilayer perceptron having six input nodes 96, a hiddenlayer having five neurons 100, and an output layer having four outputneurons 104. Each of the neural network nodes 96, 100 and 104 areconnected to nodes in an adjacent layer by means of adjustable synapticweighted connections 106. In this embodiment, the inputs comprise "peakfrequency", "peak amplitude" "range of motion" (ROM), "restricted?"(that is, whether the jaw opening is restricted or not) "percent ofopening" (at time of vibration), and "pain in TMJ?". (Yes or No) Theseinputs are processed by the neural network in a conventional manner asdescribed in more detail in R. P. Lippmann, "An Introduction ToComputing With Neural Nets", IEEE ASSP Magazine, April, 1987, pp. 4-22,which is herein incorporated by reference. The neural network 94 istrained in accordance with conventional neural network trainingprocedures.

In this application, however, it is critically important that thetraining data be properly selected. In the preferred embodiment, datafrom approximately 250 patients is used to train the neural network.These patients have conditions which have surgically and/orradiographically confirmed diagnoses. In the embodiment shown in FIG. 12the neural network 94 is trained with patient data which have confirmeddiagnoses that fall into one of four different categories: normal,displaced disc with reduction (DDR), displaced disc without reduction(DD) and degenerative joint disease (DJD). By way of example, assumethat the first training data is from a subject having degenerative jointdisease. The six parameters for that patient are input into the neuralnetwork 94 in the input nodes 96. These continuous valued and discreteinputs are processed by the neural network which generates some outputlevel (e.g., a value between zero and one) at the four output nodes 104.Since the neural network has not yet been trained these outputs willgenerally be randomly distributed. Yet this patient has degenerativejoint disease and it is desired that the neural network recognize thepattern of input parameters to indicate the classification of DJD. Thus,in accordance with a conventional neural network training techniques theinternal weights connecting the input, hidden and output nodes areadjusted in a manner to increase the output for the DJD node anddecrease the outputs for the other three nodes. In accordance with thepreferred embodiment of the present invention the backpropagationprocedure is used to train the network, as described in more detail inthe above-discussed Littmann article. Each set of patient training datamay typically require twenty to thirty thousand training iterations toachieve an acceptable level of performance.

It should be noted that the Neuroshell neural network product used inthe preferred embodiment, allows the user to set up a training set and atest set at the same time. This permits the system to perform aself-test at various intervals, for example, at every one thousanditerations. The test set consists of a separate representative patientdata set for which the answers (correct diagnosis) are known. The systemthen can check it's performance to determine whether it is yet producingthe correct output response or whether it needs further training. Oncethe error rate has reached a minimum the training session is terminatedfor that patient data and it is determined that the neural network 94 isnot over-trained. That is, training is stopped at the point of bestgeneralization, before the training set is too well memorized.

Once trained, new patient data with an unknown condition can be taken,processed and input into the neural network. The output indicated by theoutput nodes 104 will each form a value between zero and one indicatinghow well the current patient's data matches classifications in thetraining set. It should be noted that this output is subject to anaccuracy which can be measured by the performance on a new validationtest set. Thus, if it is known that a particular classification is 95percent reliable then the output is multiplied by 0.95 to give anindication of the probability of that answer being correct.

Referring now to FIG. 13 another preferred embodiment of a neuralnetwork 108 used with the present invention is shown. The neural networkis shown as a single box 108, for simplicity, however, it will beappreciated that it is essentially the same architecture as neuralnetwork 94 shown in FIG. 12 with additional input and output nodes. Inthis embodiment, the neural network 108 includes 11 inputs and 10outputs. In this embodiment, the inputs "restricted" and "pain in TMJ"have been eliminated since training of the neural network on patientdata has indicated that these are not important to the classification.That is, after training, low weights on weighted connections 106 areassociated with those inputs. Restriction is a subjective yes/no (1 or0) input. Lateral deviation is a continuous measure of left-rightposition of the jaw with respect to centric occlusion.

To explain the outputs in more detail, the classification "normal/quiet"describes a TM joint with a normal disk/condyle relationship(non-displaced) and a remarkably low amplitude of vibration across themeasured frequency spectrum (20 to 650 Hz). The range of motion to wideopening is generally more than 40 mm (40 to 60 mm), the mandible doesnot deflect to one side at maximum opening more than 1-2 mm and thepatient is able to move 10 mm or more in lateral excursion to theopposite side.

"Disk movement" describes a TM joint with a disk with either loosenessin the joint capsule or a partial displacement and reduction. A moderateamplitude of low frequency vibrations (below 300 Hz), a normal range ofmotion (wide opening more than 40 mm) and a lateral excursion toward theopposite side (more than 10 mm) are present. Looseness most often occursat or near mid-opening/mid-closing. A partial (incomplete) displacementwith reduction or a small rotation of the disk can occur at any point.

"Eminence Click" describes a TM joint with a normal disk/condylerelationship (non-displacing) that exhibits a high amplitude, shortduration, low frequency vibration (below 300 Hertz) at or near wideopening (frequently referred to as a "click" or "pop" by the patient). Anormal range of motion in opening (more than 40 mm) and in lateralexcursion to the opposite side (more than 10 mm) is seen. No indicationof a "reciprocal" vibration is present and for motions between occlusionand 75% of maximum opening, no "click" occurs at all. Thisclassification is most often confused with a reducing displaced disk(DDR) when the displacement occurs late in opening or early in closing.

"Disk displacement with reduction" (DDR) describes a TM joint with adisk that is displaced (anteriorly, medially, or antero-medially) withrespect to the condyle, usually in the closed position. The disksubsequently reduces to a "normal relationship" during opening, and indoing so it causes a short duration, high amplitude, low frequency(below 300 Hz) vibration (clinically a "click" or "pop" is detected). Areciprocal vibration (at times palpable) often occurs at the point inclosing where the disk displaces again. A normal range of opening andlateral excursion is present.. A late opening/early closing DDR may beconfused with an Eminence Click.

"Displaced disk with reduction" (with degenerative joint disease (DJD))describes a TM joint with a displaced disk (anteriorly, medially, etc.)that reduces to a "normal" relationship to the condyle, usually duringopening. Considerable degenerative changes in the disk and thesurrounding cartilage further compromise the TM joint's function. Shortduration, high amplitude, low frequency vibrations are accompanied bysimultaneous long duration, lower amplitude, high frequency vibrations.A normal range of motion, but with lateral deviations is typicallyrecorded. This classification is most similar to Advanced DJD and couldinclude a perforated disk (or posterior attachment).

"Displaced disk without reduction, quiet" ("closed lock") describes a TMjoint with an acutely displaced disk (anteriorly, medially, etc.) and arestricted range of motion in opening and lateral excursion to theopposite side. Any vibration recorded has very low amplitude frequenciesbelow 300 Hertz and represents continuous dislocation with respect tothe condyle. If the condition is unilateral, a marked deflection towardsthe affected joint will occur during maximum opening, A condition thatis often intermittent, "closed lock" may alternate with a displaced diskwith reduction from one day/week to the next.

"Displaced disk without reduction (with vibration)" describes a TM jointwith a displaced disk (anteriorly, medially, etc.) that does not reduce.Moderately restricted opening and lateral excursions to the oppositeside coincide with low frequency vibrations of moderate amplitude. Diskmovement with respect to the condyle is present (not reduction), but nohigh frequencies are seen that would suggest rough surfaces rubbingtogether during function. This classification is usually associated withlong term displacement of the disk, but with successful adaptationrather than degeneration. This classification is most similar to "DiskMovement" (looseness in normal joint).

"Displaced disk without reduction (with DJD)" describes a disk that isdisplaced in relation to the condyle (anteriorly, medially, etc.) anddoes not reduce (chronic condition). Moderate amplitude vibrationsappear all across the frequency spectrum (low to high), indicating roughsurfaces within the joint rub against one another during function. Thelevel of degeneration of the disk and associated cartilaginous tissue ismeasurable. Restriction of condylar translation may not be as noticeableas with Displaced Disk (with vibration), but the adaptation of the joint(to disk displacement) has been less successful. This classification ismost similar to DJD (early).

The category "degenerative joint disease early" describes a TM jointwith a primary clinical characteristic of a "crepitus" vibration ofmoderate amplitude all across the frequency spectrum (low to high). Anormal or near normal range of motion is seen during opening and lateralexcursion to the opposite side. The patient usually reportssignificantly impaired function and is not able to produce rapid, smoothmovements of the mandible. If disc displacement exists it is permanentand no longer restricts condylar translation. This classification ismost similar to disc displacement, without reduction with DJD.

The category "degenerative joint disease advanced" describes a TM jointwith a greatly compromised function capability. The patient usuallyexhibits a normal or near normal range of motion in opening and lateralexcursion to the opposite side. However, high amplitude, continuousvibrations, all across the frequency spectrum (low to high) are seen andthe patient has difficulty in performing very simple functional tasks.This classification is an extension of DJD (mild/early to moderate) andwould be more likely to include perforated discs and post-surgical cases(meniscectomy, etc.) where the degenerative process has not been halted.

Referring now to FIG. 14, examples of output data from the neuralnetwork 108 are shown. In this example, which results from the table ofinput data shown in FIG. 11, and the curves in FIGS. 9 and 10, the leftjoint indicates a 0.36 output for the "normal with vibration" output anda 0.85 value for the "early degenerative joint disease" output. The 0.36is a relatively low value indicating a low probability that thatcondition is the correct classification of the patient. It should benoted that in the preferred embodiment, only the top two choices aredisplayed so that the third choice would be lower than the secondchoice. In this case, the high probability of 0.85 is a relatively highprobability for "early degenerative joint disease" indicating that to belikely the correct diagnosis.

It should be noted that the categories are not necessarily mutuallyexclusive. The "displaced disc with reduction" is of course mutuallyexclusive with respect to the "displaced disc without reduction" sinceyou cannot have both a reduction and not have a reduction. Also, the"eminence click" is usually mutually exclusive with respect to the"displaced disc with reduction". A patient is not likely to have both ofthose at the same time. However, "degenerative joint disease" and"normal with vibration" are not mutually exclusive and it is possiblethat they are both present at the same time. For the right joint displayin FIG. 14 it can be seen that a value of 0.23 is produced by the"displaced disc without reduction with vibration" output and a value of0.95 for "advanced degenerative joint disease" indicating a highprobability that that is the correct diagnosis.

It should be noted that one advantage of the use of neural networks inthis application is that it does not force the system to produce a"correct" answer, or make a "best guess." Therefore, it is possible tohave more than one high probability answer. This would be the caseparticularly where these two conditions may co-exist simultaneously.Also the condition may not fall exactly into any of the categories (butinto the overlap of two categories) in which case by process ofelimination one can eliminate many of the possibilities and the task ofthe clinician is to investigate the (two indicated) possibilities.Another possibility would be low numbers for all outputs which wouldindicate that the correct diagnosis is something other than thoseproduced by the outputs. Thus, as described above, the vibrationalpatterns from a joint create a kind of fingerprint that is related towhat is going on in the joint.

It should be noted that the neural network interpreters 64, 94, 108 and136 used in the present invention are comprised in the preferredembodiment a multi-layer, feed-forward network trained with abackpropagation procedure. One advantage of this approach is that feedforward is a relatively fast single pass process. Also, since the neuralnetwork is non-linear it is able to solve complex problems which othertypes of processors have difficulty with. Since it is a supervisedneural network it is trained with known data which permits the use ofthe wealth of data which is available regarding patient conditions.

Furthermore, the backpropagation training technique has been found to bewell suited to the task of joint classification. It should be notedhowever that other types of neural networks may be successfully employedwith the present invention.

Furthermore, the adaptive interpreter of the present invention may alsocomprise a non-neural network processor. For example, a fuzzy logicprocessor may also be used in certain situations. However, it should benoted that fuzzy logic is generally used when discrete, yes or no, typeinputs are used. In the present invention typically the inputs arecontinuous valued inputs which are generally more suitable for neuralnetworks than for fuzzy logic interpreters. However, there may besituations, such as where the inputs are discrete, where the fuzzy logicinterpreter may be suitable. For further details of fuzzy logicinterpreters see Kosko, B., "Fuzzy Systems as Universal Approximators",Proc. of IEEE Fuzz-92, March, 1992, and Kosko, B., Neural Networks andFuzzy Systems, Prentice Hall, 1992, which are herein incorporated byreference.

An alternative to the neural network approach would be to use an expertsystem or rule based approach instead of a neural network or fuzzy logicinterpreter. Generally, one problem with the rule based approach is thatarbitrary threshold values must generally be established to decide, forexample, that if a value is greater than "X" amount, it means one thing,and if it is less than that amount it means another. Since in the realworld the transition is rather fuzzy, the rule based approach may not bethe best to use. However, in certain situations it may be appropriate.

It should be noted that the sounds generated by displaced disks in theTM joint are relatively straightforward to diagnose. A displaced diskgenerally makes a relatively clearly diagnosable sound. One reason forthis is the character of the TM joint which has an extreme amount oftranslation (forward and backward movement) during articulation. Thishigh degree of translation is unique to the TM joint. Other jointsgenerally only move in rotation during articulation. Because of thisthere has been much less success in diagnosing other conditions (such asdegenerative joint disease) in the TM joint, since the sounds generatedthereby are much more subtle and difficult to define. Likewise, therehas been much less success in diagnosing conditions in the joints otherthan the TM joint. Other joints do not have the clearly distinguishablesounds of the displaced disk since they do not have disks, or theextreme translation as does the TM joint. Thus, common conditions suchas degenerative joint disease in non-TM joints do not exhibit sucheasily recognizable sounds.

Further, many of these other joints, such as the knee and hip, are loadbearing joints which are comprised of (load bearing) hyaline cartilage.In contrast, the TM joint is comprised (non-load bearing)fibrocartilage. There are a number of distinctions between hyalinecartilage and fibrocartilage. Hyaline cartilage has low vascularity andthus will not readly heal when torn. In contrast, fibrocartilage hashigher vascularity, and will heal more readily when torn. Consequently,it is believed that these distinctions contribute to the qualitativedifferences between the vibrations emitted by the TM joint as opposed toload-bearing joints.

In any event, there has been very limited success in diagnosing non-TMjoints using vibrations. The results to date appear to rely heavily onthe experience and expertise of the individuals conducting the tests.Consequently, the previously applied techniques have not shownsignificant promise for wide clinical acceptance due to the subjectivenature of the interpretation as well as the high level of skill requiredto interpret the results. To overcome these shortcomings, the jointdiagnostic system of the present invention can be applied todegenerative joint disease and other conditions in non-TM joints, and inparticular to load-bearing, hyaline cartilage joints.

In accordance with a second embodiment of the present invention thediagnostic techniques are applied to the task of classifying vibrationsfrom a knee joint. Referring now to FIG. 15 there is shown a knee joint110 with medial 112 and lateral 114 sensors attached to the skin 116immediately around the joint. In contrast to the diagnostic system 44 ofthe TM joint diagnostic system 54 shown in FIG. 5, the knee diagnosticsystem 118 in FIG. 15 includes a third sensor 120 positioned adjacent tothe patella. The three sensors are connected to three amplifiers 122,124 and 126 respectively, which are coupled to a multiplexer 128. Agoniometer 130 is also connected to the multiplexer 128. It will beappreciated that a goniometer is a well known device which mechanically(or electrically) measures the angle between the bones connected to ajoint. The multiplexer output is transmitted to an analog to digitalconverter 132 which transmits the signals from the sensors or from thegoniometer to a host computer 134. A neural network 136 as describedabove is connected to the host computer 134. The host computer 134includes a preprocessor 135 and a CPU 137. The output may be displayedeither on a printed 138 or a CRT display 140.

Vibration signatures of specific conditions of the knee have beenreported. See for example, McCoy, G. et al, "Vibration Arthrography As ADiagnostic Aid In Diseases Of The Knee", J. of Bone and Joint Surgery,Vol. 698, No. 2, March 1987, pp. 288-293 which is herein incorporated byreference. Signature parameters such as those reported in this study, inaddition to above-discussed parameters (e.g., integral, integral ratio,median frequency, etc.) can be used in conjunction with the diagnosticsystem 118 of FIG. 15 to train the neural network 136 in a manner asdiscussed above in connection with diagnostic system 44.

It should be noted that once trained, the weights thereby derived can beinserted into other similar diagnostic systems to avoid repeating thetraining process. Particularly where the neural network is embodied inthe hardware this will permit the use of simplified fixed weight neuralnetwork versions, which may then be mass-produced.

The diagnostic system of the present invention may be used to diagnosevarious kinds of joints and for a number of purposes. For example, inthe field of knee diagnostics with respect to the tibiofemoral joint thefollowing (and other) conditions might be diagnosed:

a) general condition of articular cartilage and monitoring ofdegenerative or healing changes over time;

b) meniscal lesions (tears);

c) loose bodies;

d) chondromalacia;

e) arthritis;

f) osteochondritis dissecans (a lesion of subchondral bone withlocalized necrosis);

g) torn ligaments;

h) verifying the efficacy of treatments such as abrasive chondroplasty.

With respect to the patella femoral joint the following are among theuses:

a) chondromalacia;

b) osteoarthritis;

c) patella femoral malalignment and dynamic tracking problems;

d) verifying efficacy of corrective procedures for tracking problemssuch as a lateral retinacular release.

In hip diagnostics the present invention can be used for the detectionof hip abnormalities such as congenital dysplasia hip, which is not adegenerative joint disease. Also, it can be used for the detection andmonitoring of degenerative joint disease such as arthritis or avascularnecrosis. In the field of shoulder diagnostics it will be a usefuldiagnosis of arthritis. Also, by having the patient move his arm in aspecific pattern it may be possible to detect rotor cuff tears and otherlesions.

In the field of elbow, wrist and ankle diagnostics the present inventionwill be useful for general condition evaluation of articular cartilageand monitoring of degenerative or healing changes over time. Inaddition, it can be used for the detection of ligament damage. In thefield of joint prostheses performance diagnostics, the present inventioncan be used for example: for the detection of "looseness" betweenimplant components and the bone. In the hip this may include femoralstem component and acetabular cup. In the knee this may include femoralcomponents, tibial components and patella components. In the shoulderthis may include humeral, or glenoid components.

Quantitative analysis of the implant to bone interface may also bepossible. This would include determining if the implant was anchoredfirmly by bony tissue or if it is bonded to the bone by a fibroustissue; for the detection of disbonding between the bone cement andeither the implant or surrounding bone; analysis of the tracking ofimplant components as they articulate against one another. This couldinclude patella button tracking, patella clunk syndrome, engagementangle of posterior stability spike or femoral ball from acetabularsocket dislocation; detection of knee or hip implant failure due topolyethylene wear resulting in metal to metal contact duringarticulation; and detection of disassociation or loosening of modularcomponent assemblies such as polyethylene to acetabular cup interfacesand tibial polyethylene bearing to tibial tray interfaces and failure ofassociated locking mechanisms. These vibrations may be generated duringarticulation of the joint or when the patient's weight is applied andremoved from the affected joint.

Also, the present invention may be used for diagnosing conditions (suchas looseness, mechanical failure or detachment) of other implantableorthopedic devices such as fracture fixation devices. These may include,for example, bone plates, compression hip screws, intramedullary nails,etc. Since these devices may be used at non-joint locations, othermovements besides joint movement may be necessary to cause them togenerate vibrations. For example, the patient may load and unload thedevice (by applying weight to the affected bone) to cause the device tovibrate.

Other general uses for the present invention include the monitoring ofthe progression of any time varying condition of a joint. Thisprogression may be a degeneration or perhaps a healing process. Also,the changes in a joint condition due to a controlled treatment programsuch as surgical correction procedures such as lateral retinacularrelease or tibiofemoral abrasionplasty, exercise or drug therapy couldbe monitored by periodic examination. The recordings of the jointvibrations could be analyzed and compared to detect trends.

From the foregoing it will be appreciated that the present inventionprovides a diagnostic tool for classifying joint conditions which isnon-evasive, inexpensive, fast and easy to use. The joint diagnostictool utilizes joint vibrations to arrive at a non-subjective jointdisorder classification that is not dependent upon the skill of theperson conducting the test. It can be utilized by persons withoutparticular expertise in analyzing the joint vibrational patterns. Thoseskilled in the art can appreciate that other advantages can be obtainedfrom the use of this invention and that modification may be made withoutdeparting from the true spirit of the invention after studying thespecification, drawings and following claims.

What is claimed is:
 1. A system for classifying degenerative jointdisease conditions, the system comprising:a) sensor means for detectinga vibration pattern from the joint; b) preprocessor means for providinga predetermined set of data parameters descriptive of the vibrationpattern; and c) trainable adaptive interpreter means for receiving saiddata parameters as input and producing an output which indicates atleast one classification of the degenerative disease condition of saidjoint.
 2. The system of claim 1, wherein said preprocessor meansincludes means for determining the frequency spectrum of said vibrationpattern.
 3. The system of claim 2, wherein said preprocessing meansincludes means for determining an integral of the frequency spectrum ofsaid vibration pattern.
 4. The system of claim 2, wherein saidpreprocessing means includes means for determining the median frequencyof said vibration pattern.
 5. The system of claim 1, further comprisingmeans for measuring the instantaneous position of the joint duringmovement of the joint.
 6. The system of claim 1, further comprisingmeans for measuring the velocity of the joint during movement of thejoint.
 7. The system of claim 1, further comprising means fordetermining the range of motion of said joint, said range of motionbeing received by said interpreter as input.
 8. The system of claim 1,wherein said interpreter comprises a neural network.
 9. The system ofclaim 8, wherein said neural network is a multi-layer perceptron. 10.The system of claim 8, wherein said neural network contains adjustableweights which are generated during a training session using vibrationpatterns from joints with known conditions.
 11. The system of claim 8,wherein said neural network contains fixed weights generated from atraining session using vibration patterns from joints with knownconditions.
 12. A method for classifying degenerative joint diseaseconditions, said method comprising the steps of:a) detecting a first setof vibration patterns from one or more joints, the joints having a knowncondition; b) using said first set of vibration patterns and said knownjoint conditions to train an adaptive interpreter to produce an outputindicating particular known degenerative joint disease conditions inresponse to particular vibration patterns; c) detecting a second set ofvibration patterns from a subject joint; and d) transmitting said secondset of vibration patterns to said adaptive interpreter wherein saidadaptive interpreter generates an output indicating the presence of oneor more of said known degenerative joint disease conditions in saidsubject joint.
 13. The method of claim 12, wherein the step of usingsaid first set of vibration patterns and said known joint conditions totrain an adaptive interpreter further comprises the steps ofinteractively processing said first set of vibration patterns in aneural network until the output indicates the known joint condition. 14.The method of claim 13, wherein said step of interactively processingcomprises a backpropagation training procedure.
 15. The method of claim12, wherein the joint comprises two portions on opposite sides of thejoint and said step of detecting further comprises the step of placingone sensor adjacent to each joint portion.
 16. The method of claim 12,further comprising the step of detecting the frequency spectrum of thevibration pattern.
 17. The method of claim 16, further comprising thestep of an integral of the frequency spectrum of the vibration pattern.18. The method of claim 17, further comprising the step of the samplingonly a portion of said detected vibration pattern, said portionbeginning a period before and ending a period after said peak amplitudewherein only said sampled portion of first and second set of vibrationpatterns is transmitted to said interpreter.
 19. The method of claim 18,further comprising the step of repeating the step of sampling aplurality of times and averaging said sampled vibration pattern beforetransmitting to the interpreter.
 20. The method of claim 12, furthercomprising the step of measuring the instantaneous position of the jointduring movement thereof.
 21. The method of claim 12, further comprisingthe step of measuring the velocity of the joint during movement thereof.22. The method of claim 12, wherein said adaptive interpreter is aneural network and wherein the step of using said first set of vibrationpatterns and said known joint conditions to train an adaptiveinterpreter includes the step of employing backpropagation to train theadaptive interpreter.
 23. A system for classifying a trauma inducedjoint condition said system comprising:a) sensor means for detecting avibration pattern from the joint; b) preprocessor means for providing apredetermined set of data parameters descriptive of the vibrationpattern; and c) trainable adaptive interpreter means for receiving saiddata parameters as input and producing an output which indicates atleast one classification of the trauma induced condition of said joint.24. A system for classifying joint conditions in a load bearing joint,the system comprising:a) sensor means for detecting a vibration patternfrom the joint; b) preprocessor means for providing a predetermined setof data parameters descriptive of the vibration pattern; and c)trainable adaptive interpreter means for receiving said data parametersas input and producing an output which indicates at least oneclassification of the condition of said joint.
 25. The system of claim24 wherein said joint condition is congenital.
 26. The system of claim24 wherein said joint is a knee.
 27. The system of claim 24 wherein saidjoint is a hip.
 28. A system for classifying degenerative joint diseaseconditions, the system comprising:a) sensor means of detecting avibration pattern from the joint; b) means for receiving non-vibrationaldiagnostic information relating to said joint; c) preprocessor means forproviding a predetermined set of data parameters descriptive of thevibration pattern and said diagnostic information; and d) trainableadaptive interpreter means for receiving said data parameters as inputand producing an output which indicates at least one classification ofthe degenerative disease condition of said joint.